Adaptive Timing in a Dynamic Field Architecture for Natural Human-Robot Interactions

A close temporal coordination of actions and goals is crucial for natural and fluent human–robot interactions in collaborative tasks. How to endow an autonomous robot with a basic temporal cognition capacity is an open question. In this paper, we present a neurodynamics approach based on the theoretical framework of dynamic neural fields (DNF) which assumes that timing processes are closely integrated with other cognitive computations. The continuous evolution of neural population activity towards an attractor state provides an implicit sensation of the passage of time. Highly flexible sensorimotor timing can be achieved through manipulations of inputs or initial conditions that affect the speed with which the neural trajectory evolves. We test a DNF-based control architecture in an assembly paradigm in which an assistant hands over a series of pieces which the operator uses among others in the assembly process. By watching two experts, the robot first learns the serial order and relative timing of object transfers to subsequently substitute the assistant in the collaborative task. A dynamic adaptation rule exploiting a perceived temporal mismatch between the expected and the realized transfer timing allows the robot to quickly adapt its proactive motor timing to the pace of the operator even when an additional assembly step delays a handover. Moreover, the self-stabilizing properties of the population dynamics support the fast internal simulation of acquired task knowledge allowing the robot to anticipate serial order errors.